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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Supply chain management

Introduction

AI in the form of Machine Learning especially is used increasingly in the SCM to improve the performance of the supply chain management process in the manufacturing sector. With the capacity to process large data sets, learn and make recommendations, artificial intelligence and machine learning is revolutionizing the conventional supply chains into intelligent and automatic systems. SCM is a process that is made of several elements that include the movement of materials from one place to another, processing of materials, and transportation of goods from one point to another. This has greatly been aided by AI, an aspect that has led to improved coordination, accuracy, and speed.

Challenges

There are, however, some challenges that can hinder the implementation of AI in SCM. Such challenges include data quality and management, technology integration, skill gap, and cost. This is because incorrect or incomplete data can be detrimental to the application of AI as it would result in inaccurate predictions. This is especially so where companies have legacy systems that may not be compatible with new technologies. Another challenge is the absence of professionals who can operate and analyze AI systems. Lastly, the expenses that come with the purchase and enhancement of AI systems are quite high, and therefore may be inaccessible to some organizations.

AI Solutions

There are several challenges that AI can solve in SCM as shown below. In the area of data quality and management for instance, AI can help in the development of data cleaning and validation algorithms. In technology integration, AI can be applied in developing a single system that can be compatible with other structures and without the need of manual handling. This is because AI can address the problem of skill deficit by assuming the challenging functions and presenting simple interfaces that can be operated by the individuals who may not have the necessary skills. Lastly, even though the initial investment on AI may be costly, the benefits of higher efficiency and lower costs on mistakes in the long run can be advantageous.

Benefits

There are a lot of advantages of using AI in SCM. It is capable of performing tasks, and thus frees resources that would otherwise be used for them, it can work with less errors than humans and it provides better predictions. It can therefore result to huge cost reductions and increased customer satisfaction. It also enables the provision of end to end visibility of the supply chain so that companies can monitor their products in real time and adapt to the shifts in demand and supply chain. Last but not the least, AI enables the identification of risks and provides solutions on possible measures to be taken in advance.

Return on Investment

The ROI of AI in SCM can be very high. A research done by McKinsey found that the companies that adopt AI in their supply chain can enhance the forecasting accuracy by 61%, reduce inventory by 15%, and transportation cost by 35%. In monetary terms, this could mean millions of dollars in savings. However, it is crucial to point out that such benefits are contingent on the effective integration of AI, which is a delicate process that needs to be strategically approached.

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